A Random Forest based predictor for medical data classification using feature ranking

Alam, Zahangir; Rahman, Md. Saifur

doi:10.1016/j.imu.2019.100180

Cited by 163 publications

(91 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Rahman, and M.S. Rahman [12] developed a new feature ranking based method for medical data classification. In this literature, a new ranker algorithm was used to rank the features in the dataset and then the random forest classifier was applied in higher ranked feature for the heart disease prediction.…”

Section: Literature Surveymentioning

confidence: 99%

A Prediction Technique for Heart Disease Based on Long Short Term Memory Recurrent Neural Network

Manur¹,

Christ²,

Pani³

et al. 2020

IJIES

View full text Add to dashboard Cite

In recent years, heart disease is one of the leading cause of death for both women and men. So, heart disease prediction is considered as a significant part in the clinical data analysis. Standard data mining techniques like Support Vector Machine (SVM), Naïve Bayes and other machine learning techniques used in the earlier research for heart disease prediction. These methods are not sufficient for effective heart disease prediction due to insufficient test data. In this research, Bi-directional Long Short Term Memory with Conditional Random Field (BiLSTM-CRF) has been proposed to increase the efficiency of heart disease prediction. The input medical data were analyzed in a bidirectional manner for effective analysis, and CRF provided the linear relationship between the features. The BiLSTM-CRF method has been tested on the Cleveland dataset to analyze the performance and compared with existing methods. The results showed that the proposed BiLSTM-CRF outperformed the existing methods in heart disease prediction. The average accuracy of the proposed BiLSTM-CRF is 90.04%, which is higher than the existing methods.

show abstract

Section: Literature Surveymentioning

confidence: 99%

A Prediction Technique for Heart Disease Based on Long Short Term Memory Recurrent Neural Network

Manur¹,

Christ²,

Pani³

et al. 2020

IJIES

View full text Add to dashboard Cite

show abstract

“…According to non-linear techniques, several researchers developed various techniques for MDC. A review of recent techniques [13][14][15][16][17][18] discussed in this section, where the advantage and the drawback of these techniques are presented below.…”

Section: Literature Surveymentioning

confidence: 99%

“…M.Z. Alam, M. Saifur Rahman, and M. Sohel Rahman [17] developed a feature ranking, selection strategy and suitable classifier algorithm for MDC. The disease was predicted by using the various feature ranking strategies and random forest as final classifiers.…”

Section: Literature Surveymentioning

confidence: 99%

“…The performance evaluation of existing techniques with proposed RFE-FNN are compared in terms of accuracy, F-measure, sensitivity, specificity and AUC for four datasets which are explained in this section. Compared to the RFE-FNN with SVM+FOA [13], PCA with SVM+K-means [14], hybrid approach such as RBF+Navie Bayes+ J48 [15], WT-DBN [16] and FR-RFA [17], the best supervised approach is determined by using extensive experiments. The validation of RFE-FNN are discussed with each parameter and their results are discussed in the sub-section.…”

Section: Performance Evaluation Of Proposed Rfe-fnnmentioning

confidence: 99%

See 1 more Smart Citation

An Effective Fuzzy Neural Network with Reinforcement Learning Approach for Medical Data Classification

Prasad¹,

Francis²,

Huq³

2020

IJIES

View full text Add to dashboard Cite

Nowadays, several researchers are facing challenges on the prediction of diseases from the huge volume of medical databases. So, researchers are using data mining techniques like association rules, classification and clustering to address challenges. The physicians make the right decisions for successful diagnosis of various diseases by using the prediction. The existing work classifies the data to predict the certain diseases, but still it faces the difficulties due to overfitting in the training data. The main aim of this research work is to classify the Medical Data (MD) by developing the feature selection based approach in MD Classification as MDC. The irrelevant features are eliminated from the MD by using Recursive Feature Elimination (RFE) method, then ranked the features to reduce the computation cost of the proposed method. The ranked features from the RFE are given as input to the Fuzzy Neural Network (FNN) with Reinforcement Learning (RL), which is used for classification. The proposed RFE-FNN method has the accuracy of the 98.57%, 98.15% sensitivity, 98.64% specificity and 95.47% F-Measure in Heart disease dataset.

show abstract

“…G et al [48] proposed a feature importance index based on random forests, using random forest importance scores to gradually increase features. Zahangir Alam et al [49] used a random forest importance score to rank and extract top-ranking features. Random forests can effectively extract the importance scores of features but cannot distinguish the correlation between features.…”

Section: Feature Selectionmentioning

confidence: 99%

A Feature Analysis Based Identifying Scheme Using GBDT for DDoS with Multiple Attack Vectors

et al. 2019

View full text Add to dashboard Cite

In recent years, distributed denial of service (DDoS) attacks have increasingly shown the trend of multiattack vector composites, which has significantly improved the concealment and success rate of DDoS attacks. Therefore, improving the ubiquitous detection capability of DDoS attacks and accurately and quickly identifying DDoS attack traffic play an important role in later attack mitigation. This paper proposes a method to efficiently detect and identify multivector DDoS attacks. The detection algorithm is applicable to known and unknown DDoS attacks.

show abstract

A Random Forest based predictor for medical data classification using feature ranking

Cited by 163 publications

References 17 publications

A Prediction Technique for Heart Disease Based on Long Short Term Memory Recurrent Neural Network

A Prediction Technique for Heart Disease Based on Long Short Term Memory Recurrent Neural Network

An Effective Fuzzy Neural Network with Reinforcement Learning Approach for Medical Data Classification

A Feature Analysis Based Identifying Scheme Using GBDT for DDoS with Multiple Attack Vectors

Contact Info

Product

Resources

About